Lars
33b08a8d82
- Changed SQL queries in `build_sleep_duration_quality_chart_payload` and `build_sleep_debt_chart_payload` to select `duration_minutes` instead of `total_sleep_min`. - Updated calculations for sleep duration and quality scores to reflect the new field names, ensuring accurate data representation in the recovery charts.
457 lines
14 KiB
Python
457 lines
14 KiB
Python
"""
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Chart.js-Payloads für Recovery (R1–R5) — gemeinsam mit routers/charts und recovery-dashboard-viz.
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Ausgelagert aus routers/charts.py (Issue 53 / Layer 1).
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"""
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from __future__ import annotations
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from datetime import datetime, timedelta
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from typing import Any, Dict
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from db import get_db, get_cursor
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from data_layer.recovery_metrics import (
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calculate_hrv_vs_baseline_pct,
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calculate_recovery_score_v2,
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calculate_rhr_vs_baseline_pct,
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calculate_sleep_debt_hours,
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get_sleep_duration_data,
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get_sleep_quality_data,
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)
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from data_layer.utils import calculate_confidence, safe_float, serialize_dates
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def build_recovery_score_chart_payload(profile_id: str, days: int) -> Dict[str, Any]:
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if days < 7:
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days = 7
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if days > 90:
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days = 90
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current_score = calculate_recovery_score_v2(profile_id)
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if current_score is None:
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return {
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"chart_type": "line",
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"data": {"labels": [], "datasets": []},
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"metadata": {
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"confidence": "insufficient",
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"data_points": 0,
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"message": "Keine Recovery-Daten vorhanden",
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},
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}
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cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
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with get_db() as conn:
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cur = get_cursor(conn)
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cur.execute(
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"""SELECT date, resting_hr, hrv
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FROM vitals_baseline
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WHERE profile_id=%s AND date >= %s
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ORDER BY date""",
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(profile_id, cutoff),
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)
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rows = cur.fetchall()
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if not rows:
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return {
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"chart_type": "line",
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"data": {
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"labels": [datetime.now().strftime("%Y-%m-%d")],
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"datasets": [
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{
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"label": "Recovery Score",
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"data": [current_score],
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"borderColor": "#1D9E75",
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"backgroundColor": "rgba(29, 158, 117, 0.1)",
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"borderWidth": 2,
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"tension": 0.3,
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"fill": True,
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}
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],
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},
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"metadata": {
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"confidence": "low",
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"data_points": 1,
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"current_score": current_score,
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},
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}
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labels = [row["date"].isoformat() for row in rows]
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values = [min(100, max(0, safe_float(row["hrv"]) if row["hrv"] else 50)) for row in rows]
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return {
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"chart_type": "line",
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"data": {
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"labels": labels,
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"datasets": [
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{
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"label": "Recovery Score (proxy)",
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"data": values,
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"borderColor": "#1D9E75",
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"backgroundColor": "rgba(29, 158, 117, 0.1)",
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"borderWidth": 2,
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"tension": 0.3,
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"fill": True,
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}
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],
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},
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"metadata": serialize_dates(
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{
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"confidence": calculate_confidence(len(rows), days, "general"),
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"data_points": len(rows),
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"current_score": current_score,
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"note": "Score based on HRV proxy; true recovery score calculation in development",
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}
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),
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}
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def build_hrv_rhr_baseline_chart_payload(profile_id: str, days: int) -> Dict[str, Any]:
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if days < 7:
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days = 7
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if days > 90:
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days = 90
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cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
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with get_db() as conn:
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cur = get_cursor(conn)
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cur.execute(
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"""SELECT date, resting_hr, hrv
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FROM vitals_baseline
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WHERE profile_id=%s AND date >= %s
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ORDER BY date""",
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(profile_id, cutoff),
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)
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rows = cur.fetchall()
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if not rows:
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return {
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"chart_type": "line",
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"data": {"labels": [], "datasets": []},
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"metadata": {
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"confidence": "insufficient",
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"data_points": 0,
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"message": "Keine Vitalwerte vorhanden",
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},
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}
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labels = [row["date"].isoformat() for row in rows]
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hrv_values = [safe_float(row["hrv"]) if row["hrv"] else None for row in rows]
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rhr_values = [safe_float(row["resting_hr"]) if row["resting_hr"] else None for row in rows]
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hrv_baseline = calculate_hrv_vs_baseline_pct(profile_id)
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rhr_baseline = calculate_rhr_vs_baseline_pct(profile_id)
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hrv_filtered = [v for v in hrv_values if v is not None]
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rhr_filtered = [v for v in rhr_values if v is not None]
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avg_hrv = sum(hrv_filtered) / len(hrv_filtered) if hrv_filtered else 50
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avg_rhr = sum(rhr_filtered) / len(rhr_filtered) if rhr_filtered else 60
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datasets = [
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{
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"label": "HRV (ms)",
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"data": hrv_values,
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"borderColor": "#1D9E75",
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"backgroundColor": "rgba(29, 158, 117, 0.1)",
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"borderWidth": 2,
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"tension": 0.3,
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"yAxisID": "y1",
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"fill": False,
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},
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{
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"label": "RHR (bpm)",
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"data": rhr_values,
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"borderColor": "#3B82F6",
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"backgroundColor": "rgba(59, 130, 246, 0.1)",
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"borderWidth": 2,
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"tension": 0.3,
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"yAxisID": "y2",
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"fill": False,
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},
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]
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return {
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"chart_type": "line",
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"data": {"labels": labels, "datasets": datasets},
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"metadata": serialize_dates(
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{
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"confidence": calculate_confidence(len(rows), days, "general"),
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"data_points": len(rows),
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"avg_hrv": round(avg_hrv, 1),
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"avg_rhr": round(avg_rhr, 1),
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"hrv_vs_baseline_pct": hrv_baseline,
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"rhr_vs_baseline_pct": rhr_baseline,
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}
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),
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}
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def build_sleep_duration_quality_chart_payload(profile_id: str, days: int) -> Dict[str, Any]:
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if days < 7:
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days = 7
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if days > 90:
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days = 90
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duration_data = get_sleep_duration_data(profile_id, days)
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quality_data = get_sleep_quality_data(profile_id, days)
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if duration_data["confidence"] == "insufficient":
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return {
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"chart_type": "line",
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"data": {"labels": [], "datasets": []},
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"metadata": {
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"confidence": "insufficient",
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"data_points": 0,
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"message": "Keine Schlafdaten vorhanden",
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},
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}
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cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
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with get_db() as conn:
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cur = get_cursor(conn)
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cur.execute(
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"""SELECT date, duration_minutes
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FROM sleep_log
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WHERE profile_id=%s AND date >= %s
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ORDER BY date""",
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(profile_id, cutoff),
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)
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rows = cur.fetchall()
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if not rows:
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return {
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"chart_type": "line",
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"data": {"labels": [], "datasets": []},
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"metadata": {
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"confidence": "insufficient",
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"data_points": 0,
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"message": "Keine Schlafdaten",
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},
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}
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labels = [row["date"].isoformat() for row in rows]
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duration_hours = [
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safe_float(row["duration_minutes"]) / 60 if row["duration_minutes"] else None for row in rows
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]
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quality_scores = [(d / 8 * 100) if d else None for d in duration_hours]
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datasets = [
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{
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"label": "Schlafdauer (h)",
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"data": duration_hours,
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"borderColor": "#3B82F6",
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"backgroundColor": "rgba(59, 130, 246, 0.1)",
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"borderWidth": 2,
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"tension": 0.3,
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"yAxisID": "y1",
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"fill": True,
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},
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{
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"label": "Qualität (%)",
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"data": quality_scores,
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"borderColor": "#1D9E75",
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"backgroundColor": "rgba(29, 158, 117, 0.1)",
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"borderWidth": 2,
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"tension": 0.3,
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"yAxisID": "y2",
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"fill": False,
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},
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]
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return {
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"chart_type": "line",
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"data": {"labels": labels, "datasets": datasets},
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"metadata": serialize_dates(
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{
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"confidence": duration_data["confidence"],
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"data_points": len(rows),
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"avg_duration_hours": round(duration_data["avg_duration_hours"], 1),
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"sleep_quality_score": quality_data.get("quality_score", 0),
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}
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),
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}
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def build_sleep_debt_chart_payload(profile_id: str, days: int) -> Dict[str, Any]:
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if days < 7:
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days = 7
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if days > 90:
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days = 90
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current_debt = calculate_sleep_debt_hours(profile_id)
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if current_debt is None:
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return {
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"chart_type": "line",
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"data": {"labels": [], "datasets": []},
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"metadata": {
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"confidence": "insufficient",
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"data_points": 0,
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"message": "Keine Schlafdaten für Schulden-Berechnung",
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},
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}
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cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
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with get_db() as conn:
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cur = get_cursor(conn)
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cur.execute(
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"""SELECT date, duration_minutes
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FROM sleep_log
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WHERE profile_id=%s AND date >= %s
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ORDER BY date""",
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(profile_id, cutoff),
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)
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rows = cur.fetchall()
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if not rows:
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return {
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"chart_type": "line",
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"data": {"labels": [], "datasets": []},
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"metadata": {
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"confidence": "insufficient",
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"data_points": 0,
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"message": "Keine Schlafdaten",
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},
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}
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labels = [row["date"].isoformat() for row in rows]
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target_hours = 8.0
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cumulative_debt = 0.0
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debt_values = []
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for row in rows:
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actual_hours = safe_float(row["duration_minutes"]) / 60 if row["duration_minutes"] else 0
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daily_deficit = target_hours - actual_hours
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cumulative_debt += daily_deficit
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debt_values.append(cumulative_debt)
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return {
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"chart_type": "line",
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"data": {
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"labels": labels,
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"datasets": [
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{
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"label": "Schlafschuld (Stunden)",
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"data": debt_values,
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"borderColor": "#EF4444",
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"backgroundColor": "rgba(239, 68, 68, 0.1)",
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"borderWidth": 2,
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"tension": 0.3,
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"fill": True,
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}
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],
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},
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"metadata": serialize_dates(
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{
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"confidence": calculate_confidence(len(rows), days, "general"),
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"data_points": len(rows),
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"current_debt_hours": round(float(current_debt), 1),
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"final_debt_hours": round(float(cumulative_debt), 1),
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}
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),
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}
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def build_vital_signs_matrix_chart_payload(profile_id: str, days: int) -> Dict[str, Any]:
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if days < 7:
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days = 7
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if days > 30:
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days = 30
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cutoff = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
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with get_db() as conn:
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cur = get_cursor(conn)
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cur.execute(
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"""SELECT resting_hr, hrv, vo2_max, spo2, respiratory_rate
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FROM vitals_baseline
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WHERE profile_id=%s AND date >= %s
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ORDER BY date DESC
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LIMIT 1""",
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(profile_id, cutoff),
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)
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vitals_row = cur.fetchone()
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cur.execute(
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"""SELECT systolic, diastolic
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FROM blood_pressure_log
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WHERE profile_id=%s AND date >= %s
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ORDER BY date DESC, time DESC
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LIMIT 1""",
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(profile_id, cutoff),
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)
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bp_row = cur.fetchone()
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if not vitals_row and not bp_row:
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return {
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"chart_type": "bar",
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"data": {"labels": [], "datasets": []},
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"metadata": {
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"confidence": "insufficient",
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"data_points": 0,
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"message": "Keine aktuellen Vitalwerte",
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},
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}
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labels = []
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values = []
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if vitals_row:
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if vitals_row["resting_hr"]:
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labels.append("Ruhepuls (bpm)")
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values.append(safe_float(vitals_row["resting_hr"]))
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if vitals_row["hrv"]:
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labels.append("HRV (ms)")
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values.append(safe_float(vitals_row["hrv"]))
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if vitals_row["vo2_max"]:
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labels.append("VO2 Max")
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values.append(safe_float(vitals_row["vo2_max"]))
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if vitals_row["spo2"]:
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labels.append("SpO2 (%)")
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values.append(safe_float(vitals_row["spo2"]))
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if vitals_row["respiratory_rate"]:
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labels.append("Atemfrequenz")
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values.append(safe_float(vitals_row["respiratory_rate"]))
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if bp_row:
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if bp_row["systolic"]:
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labels.append("Blutdruck sys (mmHg)")
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values.append(safe_float(bp_row["systolic"]))
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if bp_row["diastolic"]:
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labels.append("Blutdruck dia (mmHg)")
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values.append(safe_float(bp_row["diastolic"]))
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if not labels:
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return {
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"chart_type": "bar",
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"data": {"labels": [], "datasets": []},
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"metadata": {
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"confidence": "insufficient",
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"data_points": 0,
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"message": "Keine Vitalwerte verfügbar",
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},
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}
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return {
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"chart_type": "bar",
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"data": {
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"labels": labels,
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"datasets": [
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{
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"label": "Wert",
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"data": values,
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"backgroundColor": "#1D9E75",
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"borderColor": "#085041",
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"borderWidth": 1,
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}
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],
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},
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"metadata": {
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"confidence": "medium",
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"data_points": len(values),
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"note": "Latest measurements within last " + str(days) + " days",
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},
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}
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